Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50021179" target="_blank" >RIV/62690094:18450/23:50021179 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10068497" target="_blank" >https://ieeexplore.ieee.org/document/10068497</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3256979" target="_blank" >10.1109/ACCESS.2023.3256979</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Popis výsledku v původním jazyce

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.

Název v anglickém jazyce

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

Popis výsledku anglicky

Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies. As a result, malware detection has always been a major worry and a difficult issue, owing to shortcomings in performance accuracy, analysis type, and malware detection approaches that fail to identify unexpected malware attacks. This paper seeks to conduct a thorough systematic literature review (SLR) and offer a taxonomy of machine learning methods for malware detection that considers these problems by analyzing 77 chosen research works related to malware detection using machine learning algorithm. The research investigates malware and machine learning in the context of cybersecurity, including malware detection taxonomy and machine learning algorithm classification into numerous categories. Furthermore, the taxonomy was used to evaluate the most recent machine learning algorithm and analysis. The paper also examines the obstacles and associated concerns encountered in malware detection and potential remedies. Finally, to address the related issues that would motivate researchers in their future work, an empirical study was utilized to assess the performance of several machine learning algorithms.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

45

Strana od-do

141045-141089

Kód UT WoS článku

001130213200001

EID výsledku v databázi Scopus

2-s2.0-85151320871